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Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices
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This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana.

Research Group
 Macroeconomic Forecasting

Date
  28th June 2017

Author
 Kesaobaka Molebatsi; Mpho Raboloko

Keywords
  ARIMA; ARCH; GARCH; CPI; Inflation

statement

This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.

International Journal of Economics and Finance; (2016), Vol 8, No 3


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